Leveraging Machine Learning Led Big Data Analytics to inform Consumer Behavior in the Retail Industry
DOI:
https://doi.org/10.56472/WCAI25-125Keywords:
Machine learning (ML), big data analytics, consumer behavior, the retail industry, personalization, predictive analytics, recommender systems, dynamic pricing, sentiment analysis, targeted marketing, consumer segmentation, operational efficiency, data privacy, algorithmic bias, ethical considerations in AI, inventory management, data-driven decision making, competitive advantage, consumer insights, retail technology innovationAbstract
This paper will address the application of machine learning (ML) and big data analytics to the retail industry in the context of how the technologies will be able to shape consumer behaviour and strategic business decisions. With retailers increasingly relying on sizeable amounts of consumer data, ML, and big data can provide useful insights into consumer preferences to enable companies to provide a tailored experience, manage inventory, and predict future trends. The paper lists the key ones, which are recommender systems, dynamic pricing, and sentiment analysis, and their relevance in achieving more customer interest and sales. It also addresses the benefits of exploiting such technologies, including improved performance and a competitive edge. However, the paper also addresses ethical issues and challenges, particularly, the privacy of the data, bias inside the algorithms and the excessive price of the apps. Lastly, the paper mentions the prospects of retail use of ML and big data in a transformative manner and facilitates responsible and ethical use of the technologies
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References
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